The correlation between eBird community science and weather surveillance radar‐based estimates of migration phenology

Abstract Aim Measuring avian migration can prove challenging given the spatial scope and the diversity of species involved. No one monitoring technique provides all the pertinent measures needed to capture this macroscale phenomenon – emphasizing the need for data integration. Migration phenology is a key metric characterizing large‐scale migration dynamics and has been successfully quantified using weather surveillance radar (WSR) data and community science observations. Separately, both platforms have their limitations and measure different aspects of bird migration. We sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data – of which we predict a positive correlation. Location Contiguous United States. Time period 2002–2018. Major taxa studied Migratory birds. Methods We estimated spring and autumn migration phenology at 143 WSR stations aggregated over a 17‐year period (2002–2018), which we contrast with eBird‐based estimates of spring and autumn migration phenology for 293 nocturnally migrating bird species at the 143 WSR stations. We compared phenology metrics derived from all species and WSR stations combined, for species in three taxonomic orders (Anseriformes, Charadriiformes and Passeriformes), and for WSR stations in three North American migration flyways (western, central and eastern). Results We found positive correlations between WSR and eBird‐based estimates of migration phenology and differences in the strength of correlations among taxonomic orders and migration flyways. The correlations were stronger during spring migration, for Passeriformes, and generally for WSR stations in the eastern flyway. Autumn migration showed weaker correlation, and in Anseriformes correlations were weakest overall. Lastly, eBird‐based estimates slightly preceded those derived from WSR in the spring, but trailed WSR in the autumn, suggesting that the two data sources measure different components of migration phenology. Main conclusions We highlight the complementarity of these two approaches, but also reveal strong taxonomic and geographic differences in the relationships between the platforms.


| INTRODUC TI ON
Understanding the broad-scale timing of life history events such as breeding, or the progression of seasonal migration is critical in understanding how organisms respond to changing environments (Visser & Both, 2005). While achieving a macrosystem view of phenology can be challenging, particularly when working with diverse ecological systems (e.g., high species richness), tools and techniques are available for such monitoring approaches. However, understanding the biases and limitations of these monitoring approaches is increasingly important, particularly when exploring issues related to climate change (Youngflesh et al., 2021), applied conservation , and human-wildlife conflicts (Ruiz-Gutierrez et al., 2021). Phenology is of broad interest across taxonomic groups (Cohen et al., 2018), and migration phenology of birds can serve as bellwethers for potential trophic mismatches and population declines in the face of recent changes in climate (Both et al., 2006;Charmantier & Gienapp, 2014;Hurlbert & Liang, 2012). However, quantifying and understanding these patterns at broad scales remains challenging.
Each spring and autumn, billions of migratory birds travel hundreds to thousands of kilometres between their breeding and non-breeding grounds, traversing hemispheres to track seasonal productivity La Sorte, Fink, Hochachka, DeLong, & Kelling, 2014;La Sorte & Graham, 2021;Youngflesh et al., 2021).
Given the spatial and temporal scale of movements made by migratory birds, comprehensive efforts to monitor annual avian migration represent a continual interest in macrosystem ecology. A variety of empirical resources are available, from bird-banding data (Covino et al., 2020) to tracking technologies, (Taylor et al., 2017), but also larger-scale measurements by radar  and observations provided by community (citizen) science platforms (La Sorte et al., 2013;Sullivan et al., 2014).
While weather surveillance radar (WSR) provides large spatial coverage (e.g., continental scale) and collects measurements that directly capture nocturnal in-flight migration, this tool often falls short in providing species-specific observations (Kelly & Horton, 2016).
Conversely, the strength of eBird community science data lies in the species-specific nature of the diurnal observations, although translating those observations to absolute abundance is difficult (Johnston et al., 2015). Additionally, like nearly all diurnal, groundbased observations of migratory birds, it is challenging to disentangle the migratory status of the individuals observed -especially for species where individuals of varying life history stages overlap geographically (Callaghan & Gawlik, 2015). Across these two platforms, limitations of one method may be compensated by the other, and likely the integration of these two pathways would yield a more comprehensive monitoring approach (Horton et al., 2018;Nilsson et al., 2021;Weisshaupt et al., 2021).
Community science (e.g., eBird) fills the species-specific gaps in radar and radar addresses the inability of community science to delineate periods of active migration or estimate absolute abundance.
However, it remains unclear how phenology metrics compare across the two platforms.
The 143 WSR stations located across the contiguous United States provide nearly complete aerial coverage of the country, spanning upwards of 20° of latitude. WSR stations measure the intensity of nocturnal migration via radar reflectivity, which can be used to quantify nightly, and by extension, seasonal passage of migrants.
Because the vast majority of migrants fly at night (~80% of migratory species), nocturnal WSR scans capture the bulk of migratory passage . Parallel efforts to monitor avian migration phenology have been accomplished by leveraging WSR to investigate phenological shifts on a continental scale  and through occurrence and abundance observations from the eBird community science database (Hurlbert & Liang, 2012;La Sorte & Graham, 2021;Mayor et al., 2017;Youngflesh et al., 2021). Although studies have examined broad-scale migration phenology using these two data resources, these approaches have not been paired to examine similarities and differences in their estimates.
To this end, we sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data -of which we predict a positive correlation. In addition to measuring correlation, we also explore the difference in the magnitude of phenology estimates between platforms. For instance, does one platform consistently lead to earlier or later phenology estimates?
We examine how the correspondence between the two platforms varies between spring and autumn migration, among dominant species in three taxonomic orders (Anseriformes, Charadriiformes and Passeriformes), and among WSR stations in three North American migration flyways (western, central and eastern;La Sorte, Fink, Hochachka, Farnsworth, et al., 2014). Because birds detected and measured at WSR stations in North America are largely composed of Passeriformes Horton, Nilsson, et al., 2019;Nilsson et al., 2018), we expect eBird migration phenology estimates derived for Passeriformes to be more strongly correlated with WSR estimates. Finally, we predict the correlation between the two approaches will be strongest in the eastern flyway, which Main conclusions: We highlight the complementarity of these two approaches, but also reveal strong taxonomic and geographic differences in the relationships between the platforms.

K E Y W O R D S
bird migration, citizen science, community science, eBird, phenology, radar remote sensing contains among the three flyways the highest proportion of migrating Passeriformes (La .

| ME THODS
In what follows, we describe in detail the data extraction and processing steps required to obtain our target phenology metric for both weather surveillance radar (WSR) and eBird data. For the purposes of this study, we opted to use the first date when mean abundance reaches half of its peak value as an estimate of seasonal migration phenology (Youngflesh et al., 2021). This choice reflects the need to select a robust metric capable of capturing the full range of species-specific patterns of migration across a large spatial extent.
Particularly in the case of eBird data, diurnal ground observations make it challenging to distinguish whether each observed individual is actively migrating or whether it is residing within its breeding or non-breeding grounds. On Supporting Information Figure S1, we demonstrate this variation both conceptually and for the specific case of the gray catbird (Dumetella carolinensis) in three separate locations of its annual geographic distribution.

| Radar
We used level II data from 143 WSR stations to quantify avian migration passage in both spring (1 March-15 June) and autumn (1 August-15 November) between the years 2002 and 2018. These observations were taken between dusk and dawn, capturing the movement of nocturnal migrants across North America at the five lowest elevation scans recorded by the radar (~0.5°, 1.5°, 2.5°, 3.5° and 4.5°) at 30-min intervals. These data were acquired through the Amazon Web Service portal (https://s3.amazo naws.com/noaa-nexra d-level 2/index.html). From the raw data, we isolated and preserved reflectivity classified as biological and removed any precipitation or other reflective clutter. We used MistNet, a convolutional neural network, to classify raw radar data before we aggregated or summarized any radar data (Lin et al., 2019).
From precipitation-free scans, we built profiles of vertical activity from 0 to 3,000 m above ground level at 100-m intervals, capturing migration intensity, via reflectivity, and migration speed and direction, via radial velocity. We extracted radar measures from a circular buffer centred at each WSR station with a radius of 5 to 37.5 km (i.e., a 37.5-km buffer with a 5-km buffer removed from the centre). As the radar beam propagates from the point of origin, the beam broadens, reducing spatial resolution, and additionally, distant volumes can show greater uncertainty in beam height estimates due to non-standard refraction -for this reason, we use a narrower range of data, 37.5 km (Farnsworth et al., 2016).
For each radar sample taken every 30 min, we first converted radar reflectivity factor (dBZ) to radar reflectivity (η) following Chilson et al. (2012). We used reflectivity as a measure of migration intensity and multiplied it by groundspeed to obtain the rate of migratory passage at each of the 100-m height intervals, and summed this rate through the night to yield a nightly measure of migrant passage; see  for additional details. For each WSR station, we then fit season-specific generalized additive models (GAMs) to the time series of nightly passage rates (response) and ordinal date as a smooth predictor term across all years. Using the model to predict daily passage rates, we first determined the level of maximum seasonal passage. We then estimated the date of half-max during spring and autumn, defined as the first date prior to the date of maximum passage that corresponded to half of the maximum passage ( Figure 1b). In each model, we included year as a random effect.

| eBird
eBird is a community science platform operated by the Cornell Lab of Ornithology designed to aid bird watchers in tracking and cataloguing bird sightings, while contributing to a global network of data that informs biodiversity and conservation science .
Entries are compiled into a single online database, screened by predetermined filters, and when necessary, vetted by expert regional moderators. To date, eBird is the largest ecological community science database in the world, documenting the occurrence and abundance of thousands of bird species across the globe.
Unlike traditional large-scale bird occurrence databases, eBird is a semi-structured 'big data' resource. As such, statistical models are needed to standardize estimates of occurrence and abundance across space and time based on variation in observer skill, observer effort, habitat, and geography (Johnston et al., 2021). In our eBird analysis, we used weekly relative abundance estimates generated by adaptive spatio-temporal exploratory models (AdaSTEMs), which are processed and hosted by the Cornell Lab of Ornithology Fink, Auer, Johnston, Strimas-Mackey, et al., 2020).
AdaSTEM uses training observations from 1 January 2014 to 31 December 2018 and a total of 88 predictor variables from three classes: observation-effort (n = 6), dimensions of time (n = 3), and environmental variables (n = 79). We acquired weekly AdaSTEM estimates of relative abundance (abundance_median) for a total of 610 species at a 2.96 × 2.96 km spatial resolution for the year 2018 within the Western Hemisphere using the R package ebirdst . We retained relative abundance estimates for 293 nocturnally migrating bird species .
We selected nocturnally migrating species because the bulk of the migratory species in North America migrate at night. Abundance estimates were denoted as relative because they do not capture the full complexity of species-specific absolute detection probabilities, and thus serve as an index of total count of individuals detected by an expert observer at the optimal time of day and distance to maximize detection of the species (Fink, Auer, Johnston, Ruiz-Gutierrez, et al., 2020). We extracted weekly estimates of relative abundance for each of the 293 species within a radius of 37.5 km centred at the 143 WSR stations. We then averaged these values for each species, week, and WSR station.
We estimated spring and autumn migration phenology for each species and WSR station using a generalized additive model (Wood, 2011) with weekly estimates of relative abundance as the response and ordinal date as a smooth predictor term. GAMs were fit separately for each species and season, with spring migration GAMs fit from 4 January to 28 June and autumn migration GAMs fit from 6 July to 28 December. AdaSTEM relative abundance estimates are made for the midpoints of each week. The beginning and end of our two seasonal migration periods reflect this distinction.
We extracted GAM predicted daily relative abundance estimates for each species, season, and WSR station. We then estimated the date of half-max during spring and autumn migration, defined as the first date prior to the date of maximum relative abundance that corresponded to half of the maximum predicted relative abundance ( Figure 1). If relative abundance estimates peaked early in the season (e.g., before ordinal day 10 in spring and before ordinal day 185 in autumn, Figure 1d), we selected the first date after the date of maximum relative abundance that corresponded to half F I G U R E 1 (a) Mosaic of radar reflectivity using data from 143 weather surveillance radar (WSR) stations (white circles) for the night of 12 April 2018, at approximately 05:00 hr UTC, with biological reflectivity shown in green, and weather contamination (i.e., precipitation) shown in yellow. (b) The date of half-max passage estimated from the predicted curves of nightly migration passage rates. (c) Estimated relative abundance (no. birds/km/hr) for the blue-gray gnatcatcher (Polioptila caerulea) during the week of 12 April 2018, overlaid with the locations of the 143 WSR stations (white circles). (d) Mean relative abundance of the blue-gray gnatcatcher during spring migration at a southern WSR station (KMLB in Florida) and at a northern WSR station (KOKX in New York), demonstrating the northward movement in migration and their respective dates of half-max relative abundance of the maximum predicted relative abundance. We only included half-max estimates if the adjusted R 2 from the generalized additive model was greater than 25%. Lastly, to ensure that the WSR station had the potential to detect each species while in active migration, we restricted half-max estimates to the periods between 1 March and 15 June during spring migration and between 1 August and 15 November during autumn migration.
In total, all 293 nocturnally migrating species met our model fit criteria (e.g., R 2 from GAM > 25%) in spring and 279 in the autumn.
The remaining nine orders individually accounted for up to 4% of the species (total of 14%). For the autumn migration analysis, we retained 279 nocturnally migrating species distributed between 11 taxonomic orders. Passeriformes totalled 57% of the 279 species, again followed by Charadriiformes (16%) and Anseriformes (13%). The remaining eight orders together made up the remaining 14%. The next highest order not included was Pelecaniformes in both spring and autumn, which made up only 3 and 4% of diversity, respectively. For this reason, we saw it as a breakpoint in the described results.

| Data analysis
We calculated Pearson's correlation coefficient to assess the cor- States between 103° and 90° west longitude, and the eastern flyway as the contiguous United States east of 90° west longitude . We used one-way ANOVAs to examine statistical differences in means of species richness and magnitude offset across flyways, and when appropriate, Tukey honestly significant difference (HSD) post-hoc tests to identify specific pairwise differences. All analyses and figures were generated using R version 4.0.2 (R Core Team, 2020).
We used the mgcv package to implement the generalized additive models (Wood, 2011).

| RE SULTS
We compiled WSR spring and autumn migration phenology esti-  During autumn migration, Tukey HSD post-hoc tests did not reveal any significant pairwise differences.

F I G U R E 2
Species richness of nocturnally migrating birds estimated at 143 weather surveillance radar (WSR) stations using eBird data during (a) spring and (b) autumn migration. A total of 293 nocturnally migrating bird species were considered in our analyses. The percentages of Anseriformes (c, d; n = 38), Charadriiformes (e, f; n = 47) and Passeriformes (g, h; n = 167) species richness estimated at 143 WSR stations using eBird data are shown. These values may not sum to 100 given species in other orders, that were not included in analysis, may be present. The vertical grey lines delineate the boundaries between the western, central and eastern North American migration flyways

| Differences in the magnitude of the two phenology indices
In addition to examining correlation strength, we quantified the difference in the magnitude of the two phenology indices. For all species combined across the 143 WSR stations, the eBird estimates of migration phenology occurred an average of 2.2 ± 5.4 (± SD) days earlier than the WSR estimates during spring migration ( Figure 6a). Relative to Anseriformes, spring phenology estimates were more consistent on average in Passeriformes and Charadriiformes, with eBird showing estimates that were −1.1 ± 6.2 (± SD) and 2.8 ± 6.5(± SD) days earlier than WSR estimates, respectively ( Figure 6a). Anseriformes measures were further offset, with an average difference of 26.7 ± 11.9 (± SD) days between WSR and eBird estimates (Figure 6a). The overall offset of the phenology estimates was larger during autumn than spring migration, with the WSR estimates of migration phenology averaging 8.7 ± 7.8 (± SD) days earlier than eBird across all orders (Figure 6b). Anseriformes dates trailed an average of 34.8 ± 11.2 (± SD) days behind WSR, consistent with Anseriformes migration occurring later than the other orders during autumn migration, potentially beyond the WSR sampling period (Figure 6b). Passeriformes were offset by 5.1 ± 8.0 (± SD) days on average and Charadriiformes by 2.3 ± 12.7 (± SD) days on average ( Figure 6b). We found differences across flyways on average during spring and autumn migration in the offset between the WSR and eBird estimates (spring F 2,140 = 5.96, p = .003; autumn F 2,140 = 3.76, p = .026). Specifically, Tukey HSD post-hoc tests revealed differences on average in the spring migration offset between the western and central flyways (mean difference = 3.6 days, p < .05) and between western and eastern flyways (mean difference = 3.1 days, p < .05) -in both cases the western flyway showing smaller offsets.
Tukey HSD post-hoc tests did not reveal pairwise differences during autumn migration. Interestingly, the magnitude of the offset between WSR and passerine-derived eBird phenology indices varied on average across latitudes of the contiguous United States, becoming more negative (i.e., radar earlier) with increasing latitude during spring migration (β = −0.54, p < .001, R 2 = .24) and increasing with increasing latitude during autumn migration (β = 0.44, p < .001, R 2 = .09) ( Figure 6).

| DISCUSS ION
In an era of big data, integration of complementary ecological information is critical in describing rapidly changing behaviours and populations, since shortcomings of one source may be overcome by another (Hampton et al., 2013;Isaac et al., 2020). Here, we show a positive correspondence between eBird and WSR migration phenology metrics within the contiguous United States, which could allow the two approaches to illustrate a more complete depiction of the region's avian migration system. Our results indicate that this correlation between migration phenology measured by eBird and WSR was strongest during spring migration. Moreover, phenology metrics showed almost no temporal offset during spring migration These seasonal macroscale patterns are likely driven by the region's prevailing winds (Kranstauber et al., 2015;La Sorte, Fink, Hochachka, Farnsworth, et al., 2014), and by geographic variation in vegetation phenology (La Sorte, Fink, Hochachka, DeLong, & Kelling, 2014;Ng et al., 2022), particularly in the western portion of the continent.
One of the challenges we faced in our assessment of migration phenology was how to properly characterize eBird phenology in a capacity that generalizes to all species. In any one location, species may occur during the breeding or non-breeding season or in transit during migration. Thus, phenology indices need to be robust to these dynamics. In our approach, we opted to use the date that preceded half of the maximum seasonal abundance (Youngflesh et al., 2021). Via this approach, eBird-based estimates only slightly preceded those derived from WSR in the spring, but trailed WSR in the autumn, with Anseriformes as the exception. In the case of a true passage migrant, with a diagnostic bell-shaped passage signature, this extraction approach likely captures the start of migration (i.e., leading edge of passage), rather than the peak (Youngflesh et al., 2021). WSR-based estimates, in contrast, capture individual birds in active migration (i.e., in flight), so measuring peak passage (i.e., date of max) is more likely a reliable and better approximation of the timing of system-wide peak activity. Examining the magnitude offsets between WSR and eBird, it is clear that phenology estimates for Anseriformes (waterfowl) are not a primary contributor to those captured by WSR's half-max passage dates. Because waterfowl tend to migrate early in the spring and late in autumn (La Sorte et al., 2015), it appears that Anseriformes are not as well represented in the WSR signal. The positive correspondence between Passeriformes (songbirds) and Charadriiformes (shorebirds) with WSR phenology estimates indicates their dominance in aerial passage. This outcome suggests that while using all species is valid, focusing on songbirds and shorebirds is sufficient to capture the primary characteristics of passage.

| CON CLUS IONS
This study provided a general comparison of WSR and eBird-based estimates of migration phenology, identifying key similarities and differences between the two approaches. It would be valuable to estimate these phenology metrics on an annual basis to determine how these two approaches compare and track across years. While WSR data can be used to generate annual estimates, eBird AdaSTEM products currently lack annual replicates. Expansion of these eBird products would have tremendous utility, especially in understanding migrant sensitivity to climate effects (Youngflesh et al., 2021).
Additionally, because Passeriformes were shown to be most strongly correlated with radar-derived timing events, it remains unclear if specific assemblages of migrants within this order could further refine the comparisons. Examining families or even assemblages of common species may reveal dominant signals in macroscale movement patterns that are generalizable to a few species, rather than an entire order. However, uncovering these subsets (e.g., 10 species) presents a non-trivial task in exploration, as there are many trillions of unique combinations of nocturnal species assemblages (e.g., from 293 total species). Regardless, integration across these two platforms holds promise in depicting a more complete understanding of avian migration, especially regarding important indices, like migration phenology.

AUTH O R CO NTR I B UTI O N S
EH and KGH conceived the initial idea for this study, performed statistical analyses, produced figures and results, and drafted the paper; MB provided revisions and insights on radar analyses; FAL and HMM provided additional revisions, eBird insights, and statistical input.

ACK N OWLED G M ENTS
We thank Lise Aubry for her feedback and support in EH's initial honor's thesis work. We thank the Cornell Lab of Ornithology Status and Trends Team for making eBird products and tools publicly available for analysis. Lastly, we thank the many thousands of eBird observers that make those products a possibility.

CO N FLI C T O F I NTE R E S T
We declare we have no competing interests.

E TH I C S S TATEM ENT
Approval was not required for this study.

DATA AVA I L A B I L I T Y S TAT E M E N T
The weather surveillance radar data generated during and/or analysed